جست‌وجوی استاد و پژوهشگر

• Experimental psychology
• Educational psychology
• Social psychology
• +۳ مورد دیگر

Kempei Shiina is a Professor at the Faculty of Education and Integrated Arts and Sciences, Waseda University , with a career spanning over three decades. He holds a PhD in Psychology (1994) and a Master of Arts (1983) from Waseda University. Research Focus: Experimental psychology, educational and social psychology, psychometrics, and cognitive modeling. Key Contributions: Historical analysis of rating scales, correlation coefficient anomalies, cursor trajectory-based decision analysis, and computational psychometrics. His recent work explores the historical roots of GPA systems , Jesuit evaluation methods , and the biases in Pearson’s correlation coefficients when applied to categorical data. He has pioneered the use of cursor movement analysis to infer cognitive hesitation and decision-making processes in rating tasks. Scientific Awards: Winner of the 13th Optical Illusion Contest (2021) for the 'Rotating Objects on a Checkerboard Pattern' illusion. His publications and presentations span educational psychology , cognitive science , and statistical history , with over 17 papers and 20 research projects funded by the Japan Society for the Promotion of Science. Teaching: Offers graduate courses in mathematical modeling, educational evaluation, and psychological experimentation at Waseda University. Professional memberships include the Japanese Psychological Association and the Japan Behaviormetrics Society .

• Mathematical Physics
• Condensed Matter Physics
• Quantum Zeno Effect
• +۴ مورد دیگر

Kazuya Yuasa is a Professor at the Department of Physics, School of Advanced Science and Engineering, Waseda University , where he has worked since 2013, previously holding positions as Associate Professor (2012–2013) and at the Waseda Institute for Advanced Study (2007–2012). His research spans quantum mechanics, mathematical physics, and condensed matter physics , with a focus on quantum coherence, entanglement, and control protocols. Education: Doctor of Science (2001) from Waseda University. His work on quantum Zeno dynamics, Trotter error bounds, and dynamical decoupling has advanced quantum computing and error mitigation. Key collaborations include researchers like Daniel Burgarth and Paolo Facchi, with applications in quantum optics, superconductivity, and atomic physics . The 15 most recent articles highlight his contributions to quantum simulation accuracy , Gaussian metrology , and open quantum systems . Topics range from quantum thermodynamics to entanglement generation , reflecting a blend of foundational and applied quantum research. Scientific Awards: No specific awards are listed in the provided data.

• Information Theory
• Coding Theory
• Statistical Learning Theory
• +۳ مورد دیگر

Shunsuke Horii is an Associate Professor at the Center for Data Science, Waseda University. His research spans information theory, coding theory, statistical learning theory, and data science applications. He actively collaborates with industry through initiatives like the Waseda Data Science Consortium. Education: Ph.D. in Science and Engineering from Waseda University (2009), Master's from Waseda University Graduate School of Science and Engineering (2004). Research Focus: Addresses causal effect estimation in data science using Bayesian decision theory, sparse modeling, and optimization techniques like ADMM and variational inference. Develops efficient algorithms for multiuser communication, matrix completion, and privacy-preserving distributed computing. Teaching: Instructs courses on statistics literacy, data science, and programming with Python/R across multiple academic quarters. Grants: Leads projects funded by Japan Society for the Promotion of Science, including causal inference frameworks, product recommendation systems, and business analytics. Publications: 21 papers with 61 Scopus citations, focusing on LP decoding, Bayesian hierarchical models, and statistical causal analysis.

• Genome biology
• System genome science
• Bioinformatics
• +۴ مورد دیگر

Hitoshi Iuchi serves as an Assistant Professor at the Faculty of Science and Engineering, Waseda Research Institute for Science and Engineering, Waseda University, a position held since April 2021. Previously, he conducted research at the National Institute of Advanced Industrial Science and Technology (2019-2021), establishing expertise in computational genomics and bioinformatics methodology development. His research centers on genome biology and system genome science, with specialized focus on developing algorithms for virus-host interaction prediction, temporal differential gene expression analysis, and oscillation detection in multi-omics data. He pioneers non-parametric statistical approaches and representation learning techniques to address challenges in infectious disease modeling, single-cell genomics, and circadian rhythm analysis, with particular emphasis on handling noisy time-series data and decaying molecular oscillations. Dr. Iuchi leads a Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Early-Career Scientists project (2021-2025) titled 'Algorithm development for detecting differentially expressed genes in single-cell RNA-seq', advancing computational frameworks for cellular heterogeneity analysis. His work bridges theoretical bioinformatics with practical applications in vaccine design, as evidenced by recent contributions to lipid nanoparticle-mRNA vaccine optimization through immune profiling.

• Statistical Science
• Earthquake Prediction
• Actuarial Science
• +۵ مورد دیگر

Shunichi Nomura is an Associate Professor at the Faculty of Commerce, Graduate School of Accountancy, with a PhD in Statistical Science from The Graduate University for Advanced Studies (Sokendai). His research bridges statistical methodology with applications in earthquake forecasting and actuarial science. Education: PhD in Statistical Science (Sokendai). He develops Bayesian space-time models for earthquake recurrence prediction and applies data mining techniques to insurance risk and healthcare data. His work integrates mathematical statistics with seismological observations to improve probabilistic hazard assessments. Recent publications focus on nonstationary renewal processes, slip rate inversion, and sparse estimation methods like group fused lasso. These studies span geophysics, actuarial mathematics, and machine learning applications. Scientific Awards: Actuarial Excellent Paper, The Japanese Actuarial Society (2017). He leads JSPS-funded research projects on spatiotemporal earthquake modeling and Bayesian prediction frameworks. His teaching includes advanced statistics and data science courses for actuarial students.

• Machine Learning
• Statistical Learning Theory
• Statistical Science
• +۳ مورد دیگر

Goki Yasuda is an Associate Professor at the Center for Data Science, Waseda University. His research spans statistical science, intelligent informatics, and machine learning, with specialized interests in label noise robustness, semi-supervised learning, and asymptotic analysis of algorithms. He teaches extensively in data science education, including courses like 'Statistics Literacy', 'Data Analysis with Python', and 'Modeling for Data Science' through Waseda's Global Education Center. His research explores foundational machine learning challenges, including: Theoretical analysis of classification under label noise conditions Performance limits of semi-supervised learning with unlabeled data Bayesian inference for misspecified probabilistic models Algorithm optimization for real-world data imperfections Publications demonstrate a strong emphasis on theoretical machine learning (75% of recent work) with secondary focus on applied signal processing for video coding (25% of earlier publications). Research consistently employs statistical methods to address data quality issues in learning systems. He leads research projects including: 'Developing machine learning algorithms for low-quality data' (2022) 'Research on machine learning from data of various quality' (2019) 'Performance evaluation of prediction in half-ranked learning' (2018)

• Biochemistry
• Spectroscopy
• Molecular Imaging
• +۴ مورد دیگر

Masahiro Ando is an Associate Professor at the Research Organization for Nano & Life Innovation , Waseda University, where he has worked since 2019. His academic background includes a Ph.D. in Science from the University of Tokyo (2011) and prior positions at Waseda University's Institute for Nanoscience & Nanotechnology and Consolidated Research Institute for Advanced Science and Medical Care. Education : Doctoral Degree in Science (University of Tokyo, 2011), Bachelor's Degree in Science (University of Tokyo, 2006) His research interests focus on Molecular Imaging , Raman Spectroscopy , and Intelligent Measurement Analysis . He has made significant contributions to biomedical applications of Raman spectroscopy, including detection of secondary metabolites in Streptomyces species, lipid metabolism analysis in viral infections, and structural studies of fungal spore walls. Current projects explore aortic aneurysm mechanisms via mechanotransduction and high-throughput screening of bioactive compound producers. Recent research trends from his 42 publications (h-index 21 on Scopus, 25 on Google Scholar) include label-free molecular imaging of microbial systems, lipidomics in disease models, and optimization of Raman spectral analysis for clinical and food safety applications. His work integrates multivariate curve resolution (MCR-ALS) with Raman spectroscopy for precise quantification of molecular distributions. Professional Memberships : The Spectroscopical Society of Japan, Japan Society for Molecular Science, The Chemical Society of Japan

• Stochastic Processes
• High-Dimensional Statistics
• Financial Econometrics
• +۳ مورد دیگر

Yuta Koike is an Associate Professor at the Graduate School of Mathematical Sciences, University of Tokyo . His research focuses on statistical inference for stochastic processes , particularly in high-frequency financial data and high-dimensional statistics . He has contributed to covariance estimation under non-synchronous observations, microstructure noise, and jumps, and recently explores lead-lag relationships between stochastic processes. Research Interests : Stochastic processes, high-dimensional statistics, financial econometrics, high-frequency data, probability theory. Awards : The 32nd JSS Ogawa Award The 1st ISI Tokyo Memorial Award Editorial Roles : Associate Editor for Asia-Pacific Financial Markets (2019–present), Bernoulli (2025–present), and Japanese Journal of Statistics and Data Science (2023–present). Teaching : Courses in statistical analysis, econometrics, and probability theory at the University of Tokyo, Seijo University, and Tokyo Metropolitan University. His publications span journals like Annals of Statistics , Stochastic Processes and their Applications , and Journal of Theoretical Probability . He actively presents at international conferences, including the Joint Statistical Meetings and SPA Conference .

• Theoretical Statistics
• Probability Theory
• Asymptotic Expansion
• +۱۴ مورد دیگر

Nakahiro Yoshida is a Professor at the Graduate School of Mathematical Sciences, University of Tokyo, specializing in Theoretical Statistics and Probability Theory. He leads the YUIMA Project, developing computational frameworks for stochastic differential equations. His affiliations include memberships in the Mathematical Society of Japan, Japan Statistical Society, International Statistical Institute, and Bernoulli Society, with editorial roles for several academic journals. Research Interests: His work spans asymptotic expansion theory, limit theorems, and statistical inference for stochastic processes, with applications in finance and insurance mathematics. Key areas include: Martingale expansions and Malliavin calculus Non-synchronous covariance estimation for high-frequency data Quasi-likelihood analysis for jump diffusions Stochastic numerical methods and computational statistics Statistical learning theory for econometrics and biostatistics Publication analysis reveals consistent focus on asymptotic expansion techniques for Wiener functionals and martingales (2023), adaptive estimation methods (2021), financial applications of stochastic processes (2017), and foundational work on quasi-likelihood analysis (2011-2013). Awards & Honors: Mathematical Society of Japan Autumn Prize (2024) Hiroshi Fujiwara Prize for Mathematical Sciences (2019) Japan Statistical Society Award (2009) Research Achievement Award, Japan Statistical Society (2007) Analysis Prize, Mathematical Society of Japan (2006) He leads the YUIMA Project Team, developing open-source software for stochastic differential equation analysis, with applications in quantitative finance and computational statistics.

• Data Science
• Artificial Intelligence
• Statistical Causal Inference
• +۳ مورد دیگر

Shohei SHIMIZU is a Professor at Osaka University 's Department of Reasoning for Intelligence and a Specially Appointed Professor at Shiga University 's Faculty of Data Science . With a career spanning multiple institutions and roles, he has been pivotal in advancing causal discovery methodologies. His research focuses on Statistical Causal Inference Machine Learning for Observational Data Reliability of AI Systems Industry-Academia Collaborations Recent work includes differentiable causal discovery and counterfactual explanations for complex AI models, emphasizing applications in biomedical research and financial analysis . Key trends in his publications show a strong emphasis on non-Gaussian models , hidden variables , and scalable algorithms , with recent integrations of large language models and open-source Python tools . Students and Collaborations Ph.D. students: Yuji Sakamoto, Kentaro Tagawa, Yi Jiang, Yoshimitsu Morinishi, Daigo Fujiwara Collaborations: RIKEN AIP Causal Inference Team, JST CREST Project, Tokyo Institute of Technology

HAYASHI Atsuhiroمشاهده پروفایل

استاد

• Educational Technology
• Higher Education
• Statistical Science
• +۲ مورد دیگر

HAYASHI Atsuhiro is a Professor at the Department of Socio-Industrial Management, School of Social Engineering, Nagoya Institute of Technology. He holds a Ph.D. in Engineering from Okayama University (1992) and an M.Sc. in Science from Kyushu University (1986). His career spans multiple institutions including Kyushu University (2009-2016), Tokyo Institute of Technology (2004-2007), and Nagoya Institute of Technology (2016-present). Ph.D. in Engineering, Okayama University (1992) M.Sc. in Science, Kyushu University (1986) B.Eng., Okayama University (1984) His research focuses on Computer-Based Statistics , Educational Engineering , and Higher Education Theory . He has led significant grants such as the Comprehensive University Entrance Examination Research (2021-2026) and Establishing Logical Description Education (2021-2024). His work emphasizes multi-faceted assessment , score band analysis , and educational equity . Recent publications examine entrance exam score correlations (2025), crisis management in admissions (2024), and logical writing instruction (2023). He has served on committees like the Japanese Society for Medical Education and International Federation of Classification Societies . His social contributions include advising super science high schools and designing statistical certification exams .

Synge Todoمشاهده پروفایل

استاد

• Quantum Many-Body Physics
• Computational Physics
• Quantum Computing
• +۵ مورد دیگر

Synge Todo is a Professor in the Department of Physics, Graduate School of Science at the University of Tokyo , with joint appointments at the Mathematics and Informatics Center , Institute for Solid State Physics , Institute for Physics of Intelligence , Quantum Software Project , and Next-Generation AI Research Center . Born in 1968, he earned his B.Sc. and Ph.D. from the University of Tokyo and subsequently held post-doctoral positions at ETH Zürich. Education Ph.D. (Science), University of Tokyo, 1996 B.Sc. (Physics), University of Tokyo, 1991 Research Interests Todo’s research integrates quantum many-body physics , computational physics , and quantum computing . He develops advanced Monte-Carlo algorithms , tensor-network techniques , and renormalization group methods to study strongly correlated electron systems , lattice QCD , quantum phase transitions , and machine-learning applications in physics . His recent work explores fault-tolerant quantum computing architectures , non-variational quantum ground-state preparation , and universal scaling laws in deep neural networks . Scientific Awards Prizes for Science and Technology, The Commendation for Science and Technology by MEXT Japan (April 2019) Grants & Collaborations He currently leads several JSPS KAKENHI projects, including “ Quantum-circuit design for computational materials science ” (2023-26) and “ Enhancement of detailed-balance-violating MCMC methods ” (2020-24). He also co-leads interdisciplinary teams focusing on data assimilation for materials discovery and scalable high-performance computing . Laboratories & Software Todo heads research activities in the HΦ quantum lattice model solver and the MateriApps portal, providing open-source tools for large-scale simulations in condensed-matter and materials science.

• machine translation
• analogy
• multilingual alignment
• +۷ مورد دیگر

Yves Lepage is a Professor at Waseda University's Faculty of Science and Engineering, specifically within the Graduate School of Information, Production, and Systems. He maintains an active research laboratory (lepage-lab.ips.waseda.ac.jp) and teaches courses including Example-based machine translation/NLP, Natural language processing, and Master's/Doctoral thesis supervision for the 2025 academic year. His research focuses on the application of analogical reasoning to natural language processing problems, particularly machine translation. Lepage's work spans formal analogy between strings, sentence-level analogies, morphological analysis, and multilingual systems. His research interests include machine translation, analogy, multilingual alignment, multilingual large language models, and foreign language aids. He has made significant contributions to understanding analogical density in corpora and developing methods to leverage analogies for translation, especially in low-resource scenarios. His publication record shows consistent output through 2024, with research evolving from foundational work on proportional analogy to sophisticated applications with neural networks. Recent work explores masked prompt learning for analogies, fuzzy analogies for translation, and organizing lexica into analogical grids for morphological generation across languages. Waseda University Teaching Award (Spring semester 2016) Lepage has successfully led multiple research projects funded by the Japan Society for the Promotion of Science, including "Theoretically founded algorithms for the automatic production of analogy tests in NLP" (2021-2024) and "Self-explainable and fast-to-train example-based machine translation using neural networks" (2018-2021). His work has involved international collaboration, including a 2023-2024 research period at the University of Montreal. He serves as Concurrent Researcher at the Waseda Research Institute for Science and Engineering (2024-2026) and has been active in professional organizations including the Information Processing Society of Japan and the Japanese Natural Language Processing Association.

• social psychology
• decision science
• behavioral metrics
• +۳ مورد دیگر

Kazuhisa Takemura is a Professor at Waseda University's School of Humanities and Social Sciences where he has served since 2003. He also directs the Waseda University Center for Decision Research (since 2007) and serves as Director of the Medical Safety Promotion Organization. His academic credentials include dual doctorates: a Ph.D. in System Science from Tokyo Institute of Technology (1994) and a Ph.D. in Medicine from Kitasato University (2013). Dr. Takemura's educational journey began at Doshisha University with undergraduate and graduate studies in Psychology, followed by interdisciplinary studies at Kyoto Institute of Technology. His research career shows a remarkable interdisciplinary trajectory spanning psychology, medicine, mathematics, and decision science. His research focuses on social psychology and behavioral decision theory, with specific interests in consumer behavior, risk perception, and decision-making processes. He employs diverse methodologies including eye-tracking experiments, mathematical modeling, and neuroeconomic approaches. Recent work examines multi-attribute decision strategies, consumer responses to energy labels, breast cancer screening behavior, and risk tolerance measurement across cultures. His research demonstrates consistent integration of theoretical frameworks with practical applications in consumer protection, medical decision-making, and public policy. Dr. Takemura's recent publication record shows remarkable productivity, with numerous articles in 2023-2025 across journals spanning psychology, mathematics, public health, and energy efficiency. His work reveals interdisciplinary connections between cognitive psychology, behavioral economics, and mathematical modeling of decision processes. He frequently collaborates with researchers across disciplines and countries, demonstrating global engagement in his field. Award for Science and Technology from MEXT (2024) for 'Reconstruction of Behavioral Decision Theory and Its Social Practical Research' Distinctive Achievement Award (Chikio Hayashi Award) from Behaviormetric Society (2022) Kimura Award from Transdisciplinary Federation of Science and Technology (2021) Fellow of the International Association of Applied Psychology (2018) Multiple Best Paper Awards across various academic societies Dr. Takemura serves as Editor in Chief of The Japanese Society of Theoretical Psychology journal and on editorial boards for Scientific Reports (Springer Nature) and The Japanese Society of Social Psychology. His leadership extends to directing research centers and participating in national and international research collaborations. His work bridges theoretical advancements in decision science with practical applications in consumer protection, medical decision-making, and public policy development. He leads the Waseda University Center for Decision Research, which functions as an interdisciplinary hub investigating human decision processes through psychological, economic, and mathematical perspectives. His research program demonstrates how theoretical insights about human decision-making can inform practical interventions in consumer protection, healthcare, and public policy.

• Systems Theory
• Agent-based Simulation
• Soft Systems Approach
• +۴ مورد دیگر

Professor Shingo Takahashi is a distinguished academic at Waseda University's Faculty of Science and Engineering, School of Creative Science and Engineering, specializing in complex systems analysis. With a Doctor of Science degree from Tokyo Institute of Technology, he has established himself as a leading researcher in systems theory, agent-based simulation, and soft systems approaches applied to social and organizational contexts. His work bridges theoretical frameworks with practical applications across multiple domains including urban planning, healthcare systems, organizational behavior, and innovation management. Dr. Takahashi's research interests span Systems Theory, Agent-based Simulation, Soft Systems Approach, Social Systems Engineering, and Safety Engineering. His work demonstrates a consistent focus on understanding complex interactions within social systems through computational modeling approaches. He has developed sophisticated agent-based models to analyze organizational learning, consumer behavior in medical insurance markets, urban development processes, and innovation diffusion mechanisms. His methodological contributions include the development of scenario analysis techniques for validating agent-based social simulations and the creation of frameworks for modeling micro-macro linkages in complex systems. His recent publications (2022-2024) reveal a continued evolution of his research toward practical applications in societal challenges, including urban development simulations, consensus-building processes, and policy design for sustainable societies. The papers demonstrate increasing sophistication in modeling techniques, with a particular emphasis on causal structure analysis, conflict-of-interest mapping, and evaluating synthetic population data for virtual city planning. His work maintains strong connections between theoretical frameworks and practical implementation across diverse domains. Outstanding Paper Award (2024) from the Society of Instrument and Control Engineers for research on similarity measurement indexes in urban development simulation Honorable Mention Paper (2022) from the International Simulation and Gaming Association for work on conflict-of-interest structures in consensus-building processes Professor Takahashi leads multiple significant research projects funded by the Japan Society for the Promotion of Science, including 'Deepening understanding of business phenomena by combining agent-based simulation and business theory' (2025-2029) and 'Normative Analysis of Diversity of Disruptive Innovation Processes by Agent-Based Model' (2023-2027). His work on modeling methods for social simulation that don't rely on big data demonstrates his commitment to developing robust methodologies applicable to contexts where quantitative data is limited. His research has been consistently supported by major funding bodies, reflecting the significance and impact of his contributions to the field.